International Real Estate Review

Although there have been many recent studies of the housing market and the possible housing bubble, very few studies take a micro-oriented approach. We construct a repeat-sales housing price index from a new data set for Irvine, California to understand recent trends in its housing market. Our analysis for 1984 to 2003 suggests that Irvine’s housing market did demonstrate traits of a bubble during certain periods of time. In fact, the bubble of the late 1980s and early 1990s appears to have been even more pronounced in Irvine. Our analysis does not, however, demonstrate conclusively that Irvine’s housing market has been experiencing a bubble the past few years.

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An Overview of the Forecasting Methods Used in Real Estate Housing Price Modelling

Jurnal Teknologi ◽

10.11113/jt.v73.4337 ◽

2015 ◽

Vol 73 (5) ◽

Author(s):

Mohan Munusamy ◽

Chitrakala Muthuveerappan ◽

Maizan Baba ◽

Mat Naim Abdullah @ Mohd Asmoni

Keyword(s):

Real Estate ◽

Price Index ◽

Housing Price ◽

Method Error ◽

The Past ◽

Housing Price Index ◽

Forecasting Methods ◽

Forecasting Method ◽

Critical Constraints ◽

Types Of Error

Forecasting is very fundamental in real estate where the past transactions become the evidences while decision making for the present and the future. Several techniques and validation approached that were commonly used in housing price index forecasting. Beside the appropriate forecasting method, error calculation is one of the critical constraints in accuracy out of all methods. This paper overview the available methods and the types of error being considered in forecasting techniques. Then the forecasting methods, namely Multiple Regression Analysis (MRA) and Artificial Neural Network which are highly applied in forecasting modelling are compared over its error accuracy.

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Repeat Assessed Values Model for Housing Price Index

Real Estate Management and Valuation ◽

10.1515/remav-2017-0027 ◽

2017 ◽

Vol 25 (4) ◽

pp. 25-39 ◽

Cited By ~ 3

Author(s):

Manuela Carini ◽

Marina Ciuna ◽

Manuela De Ruggiero ◽

Francesca Salvo ◽

Marco Simonotti

Keyword(s):

Real Estate ◽

Price Index ◽

Methodological Approach ◽

Housing Price ◽

Time Range ◽

Repeat Sales ◽

Hedonic Price ◽

Index Numbers ◽

Housing Price Index ◽

Innovative Methodology

Abstract This study proposes an innovative methodology, named Repeat Appraised Price Model (RAV), useful for determining the price index numbers for real estate markets and the corresponding index numbers of hedonic prices of main real estate characteristics in the case of a lack of data. The methodological approach proposed in this paper aims to appraise the time series of price index numbers. It integrates the principles of the method of repeat sales with the peculiarities of the Hedonic Price Method, overcoming the problem of an almost total absence of repeat sales for the same property in a given time range; on the other hand, the technique aims to overcome the limitation of the repeat sales technique concerning the inability to take into account the characteristics of individual properties.

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Property investor decisions using income and rental ratio signals

Journal of Property Investment and Finance ◽

10.1108/jpif-03-2020-0031 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Billie Ann Brotman

Keyword(s):

Real Estate ◽

Housing Market ◽

Discount Rate ◽

Price Index ◽

Housing Prices ◽

Housing Price ◽

Consumer Sentiment ◽

Content Type ◽

Income Ratio ◽

Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

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Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Entropy ◽

10.3390/e20110831 ◽

2018 ◽

Vol 20 (11) ◽

pp. 831 ◽

Cited By ~ 4

Author(s):

Özlem Ömer

Keyword(s):

Housing Market ◽

Maximum Entropy ◽

Price Index ◽

Housing Price ◽

Statistical Equilibrium ◽

Market Dynamics ◽

Quantal Response ◽

Housing Price Index ◽

The Us ◽

Equilibrium Approach

In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.

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Booms and Busts in Housing Market and Health Outcomes for Older Americans

Innovation in Aging ◽

10.1093/geroni/igab012 ◽

2021 ◽

Author(s):

Dahai Yue ◽

Ninez A Ponce

Keyword(s):

Older Adults ◽

Housing Market ◽

Fixed Effects ◽

Price Index ◽

Housing Prices ◽

Housing Price ◽

Health Impacts ◽

Price Dynamics ◽

Housing Price Index ◽

Housing Price Dynamics

Abstract Background and Objectives The U.S. housing market has experienced considerable fluctuations over the last decades. This study aimed to investigate the impacts of housing price dynamics on physical health, mental health, and health-related behaviors for older American outright owners, mortgaged owners, and renters. Research Design and Methods We drew longitudinal data from the 1992-2016 Health and Retirement Study and merged it to the five-digit ZIP-code level Housing Price Index. The analytic sample comprised 34,182 persons and 174,759 person-year observations. We used a fixed-effects model to identify the health impacts of housing price dynamics separately for outright owners, mortgaged owners, and renters. Results A 100% increase in Housing Price Index was associated with a 2.81 and 3.50 percentage points (pp) increase in the probability of reporting excellent/very good/good health status for mortgage owners and renters, respectively. It was also related to a lower likelihood of obesity (1.82 pp) for outright owners, and a less chance of obesity (2.85 pp) and smoking (3.03 pp) for renters. All of these relationships were statistically significant (p<0.05). Renters also experienced significantly decreased depression scores (-0.24), measured by the Center for Epidemiologic Studies Depression Scale, associated with the same housing price changes. Discussion and Implications Housing price dynamics have significant health impacts, and renters are more sensitive to fluctuations in the housing market. Our study rules out the wealth effect as the mechanism through which changes in housing prices affect older adults’ health. Our findings may inform policies to promote older adults’ health by investing in local area amenities and improving socioeconomic conditions.

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Estimating repeat sales residential price indices for Krakow

Oeconomia Copernicana ◽

10.24136/oc.2018.003 ◽

2018 ◽

Vol 9 (1) ◽

pp. 55-69 ◽

Cited By ~ 1

Author(s):

Michał Głuszak ◽

Jarosław Czerski ◽

Robert Zygmunt

Keyword(s):

Real Estate ◽

Housing Market ◽

Price Index ◽

Metropolitan Areas ◽

House Price ◽

Small Sample ◽

Main Concern ◽

Repeat Sales ◽

Price Indices ◽

House Price Indices

Research background: There are several methods to construct a price index for infrequently traded real estate assets (mainly residential, but also office and land). The main concern to construct a valid and unbiased price index is to address the problem of heterogeneity of real estate or put differently to control for both observable and unobservable quality attributes. The one most frequently used is probably the hedonic regression methodology (classic, but recently also spatial and quantile regression). An alternative approach to control for unobservable differences in assets’ quality is provided by repeat sales methodology, where price changes are tracked based on differences in prices of given asset sold twice (or multiple times) within the study period. The latter approach is applied in renown S&P CoreLogic Case-Shiller house price indices. Purpose of the article: The goal of the paper is to assess the applicability of repeat sales methodology for a major housing market in Poland. Previous studies used the hedonic methodology or mix adjustment techniques, and applied for major metropolitan areas. The most widely known example is the set of quarterly house price indices constructed by NBP — especially for the primary and secondary market. The repeat sales methodology has not been adopted with significant success to date — mainly because of concern regarding relative infrequency of transactions on the housing market in most metropolitan areas (thus a potentially small sample of repeated sales). Methods: The study uses data on repeat sales of residential transactions in Krakow from 2003 to 2015. We apply different specifications of repeat sales index construction and compare respective values to the hedonic price index for Krakow estimated by NBP. Findings & Value added: Findings suggest that repeat sales house sales indices can be used to track price dynamics for major metropolitan areas in Poland. The study suggests problems that need to be addressed in order to get unbiased results — mainly data collection mechanism and estimation procedure.

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Are There Any Housing Bubbles in Turkey?

10.36880/c13.02549 ◽

2021 ◽

Author(s):

Özge Korkmaz ◽

Ebru Çağlayan Akay ◽

Hoşeng Bülbül

Keyword(s):

Housing Market ◽

House Prices ◽

Housing Prices ◽

Housing Price ◽

Economic Fundamentals ◽

The Past ◽

Price Increases ◽

Long Time ◽

Housing Bubbles ◽

Turkish Lira

It is very important that the housing market, which meets the most basic need of people is needed for shelter from the past to the present, has a stable structure. The instability structure of the housing market is generally associated with the presence of housing bubbles. The deviation of housing prices from their basic value and not being able to be explained by economic fundamentals leads to the formation of housing bubbles. Housing bubbles can lead to permanent losses, as it may take a long time to return to normal prices. For Turkey as a developing country, it is important to identify an unstable structure in house prices discuss the basic economic factors related to this. After the global increases in housing prices, inflation, and depreciation in the Turkish lira, Turkey has become the country with the highest housing price increases globally in 2020. In the study, the presence of bubbles in the housing market for Ankara, Izmir, Istanbul, and Turkey in general, was investigated by SADF and GSADF unit root tests for the period 2010:01-2021:02. In this context, the study examines the presence of bubbles in housing prices for Ankara, Izmir, Istanbul, and Turkey in general, which are the three cities with the highest price increases. As a result of the study, the presence of bubbles in the housing market has been determined for Ankara, Istanbul, Izmir, and Turkey in general.

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House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-10-2020-0126 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yeşim Aliefendioğlu ◽

Harun Tanrivermis ◽

Monsurat Ayojimi Salami

Keyword(s):

Real Estate ◽

Price Index ◽

Housing Prices ◽

House Price ◽

Monthly Basis ◽

Data Set ◽

House Price Index ◽

Content Type ◽

Asymmetric Pricing ◽

Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

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